Data Modeling

Data Modeling

Good to have skills: SQL, Data Warehousing, ETL Concepts, Data Governance, Metadata Management Preferred Qualifications: • Experience modeling for enterprise data warehouses/lakes and supporting multiple downstream consumers (BI, analytics, data science). • Proven ability to optimize models for performance and usability, balancing normalization and denormalization trade-offs. • Familiarity with data governance practices such as naming standards, metadata management, and stewardship collaboration. • Strong stakeholder communication skills, including facilitating requirement workshops and presenting model designs. • Experience working in Agile delivery teams and contributing to iterative model evolution and change management. Key Responsibilities: • Gather and analyze business requirements to design robust conceptual, logical, and physical data models. • Create and maintain dimensional models using Star Schema to support analytics and reporting use cases. • Develop and refine logical data models with clear entities, relationships, and business definitions. • Produce physical models aligned to target platforms, considering performance, storage, and scalability needs. • Define keys, constraints, normalization/denormalization strategies, and data standards to ensure consistency and quality. • Partner with engineering and BI teams to validate models, resolve data issues, and support implementation handoffs. • Maintain documentation, data dictionaries, and model versioning to ensure traceability and reuse. • Participate in design reviews and contribute to best practices for enterprise data modeling and governance. Minimum Qualifications: • Education: BTECH, MTECH, MCA, MSC (or equivalent). • 3–5 years of experience in data modeling for analytics, reporting, or data platform initiatives. • Hands-on expertise in Star Schema, Logical Data Modeling, and Physical Modeling. • Strong understanding of relational concepts, entity-relationship modeling, and dimensional modeling principles. • Ability to translate business requirements into clear, implementable data structures and documentation.